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Parent(s):
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README.md
CHANGED
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@@ -3,8 +3,8 @@ pretty_name: Evaluation run of xzuyn/Alpacino-SuperCOT-13B
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dataset_summary: "Dataset automatically created during the evaluation run of model\
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\ [xzuyn/Alpacino-SuperCOT-13B](https://huggingface.co/xzuyn/Alpacino-SuperCOT-13B)\
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\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
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\nThe dataset is composed of
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\ evaluated task.\n\nThe dataset has been created from
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\ found as a specific split in each configuration, the split being named using the\
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\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
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\nAn additional configuration \"results\" store all the aggregated results of the\
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@@ -12,172 +12,20 @@ dataset_summary: "Dataset automatically created during the evaluation run of mod
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\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
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\nTo load the details from a run, you can for instance do the following:\n```python\n\
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from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_xzuyn__Alpacino-SuperCOT-13B\"\
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,\n\t\"
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\ },\n \"harness|
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\ \"
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\
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: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
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\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
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\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4888888888888889,\n\
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\ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n\
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\ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\
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: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04068942293855797,\n \
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\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04068942293855797\n },\n\
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\ \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.47,\n \
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\ \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \
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\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
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: {\n \"acc\": 0.4981132075471698,\n \"acc_stderr\": 0.030772653642075664,\n\
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\ \"acc_norm\": 0.4981132075471698,\n \"acc_norm_stderr\": 0.030772653642075664\n\
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\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4513888888888889,\n\
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\ \"acc_stderr\": 0.04161402398403279,\n \"acc_norm\": 0.4513888888888889,\n\
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\ \"acc_norm_stderr\": 0.04161402398403279\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
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: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \
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\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \
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\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
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: 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\
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\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
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: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
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\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
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\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.45664739884393063,\n\
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\ \"acc_stderr\": 0.03798106566014499,\n \"acc_norm\": 0.45664739884393063,\n\
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\ \"acc_norm_stderr\": 0.03798106566014499\n },\n \"harness|hendrycksTest-college_physics|5\"\
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: {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617747,\n\
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\ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617747\n\
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\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
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\ 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n\
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\ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
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: {\n \"acc\": 0.4085106382978723,\n \"acc_stderr\": 0.03213418026701576,\n\
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\ \"acc_norm\": 0.4085106382978723,\n \"acc_norm_stderr\": 0.03213418026701576\n\
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\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21929824561403508,\n\
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\ \"acc_stderr\": 0.03892431106518754,\n \"acc_norm\": 0.21929824561403508,\n\
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\ \"acc_norm_stderr\": 0.03892431106518754\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
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: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04082482904638628,\n \
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\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04082482904638628\n },\n\
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\ \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2751322751322751,\n\
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\ \"acc_stderr\": 0.023000086859068646,\n \"acc_norm\": 0.2751322751322751,\n\
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\ \"acc_norm_stderr\": 0.023000086859068646\n },\n \"harness|hendrycksTest-formal_logic|5\"\
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: {\n \"acc\": 0.31746031746031744,\n \"acc_stderr\": 0.04163453031302859,\n\
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\ \"acc_norm\": 0.31746031746031744,\n \"acc_norm_stderr\": 0.04163453031302859\n\
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\ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n\
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\ \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \
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\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
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: {\n \"acc\": 0.5161290322580645,\n \"acc_stderr\": 0.028429203176724555,\n\
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\ \"acc_norm\": 0.5161290322580645,\n \"acc_norm_stderr\": 0.028429203176724555\n\
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\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
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: 0.28078817733990147,\n \"acc_stderr\": 0.0316185633535861,\n \"\
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acc_norm\": 0.28078817733990147,\n \"acc_norm_stderr\": 0.0316185633535861\n\
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\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
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\ \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
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: 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
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: {\n \"acc\": 0.6060606060606061,\n \"acc_stderr\": 0.0381549430868893,\n\
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\ \"acc_norm\": 0.6060606060606061,\n \"acc_norm_stderr\": 0.0381549430868893\n\
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\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
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: 0.5404040404040404,\n \"acc_stderr\": 0.035507024651313425,\n \"\
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acc_norm\": 0.5404040404040404,\n \"acc_norm_stderr\": 0.035507024651313425\n\
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\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
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\ \"acc\": 0.6476683937823834,\n \"acc_stderr\": 0.03447478286414357,\n\
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\ \"acc_norm\": 0.6476683937823834,\n \"acc_norm_stderr\": 0.03447478286414357\n\
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\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
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\ \"acc\": 0.43333333333333335,\n \"acc_stderr\": 0.02512465352588513,\n\
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\ \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.02512465352588513\n\
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\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
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acc\": 0.24814814814814815,\n \"acc_stderr\": 0.0263357394040558,\n \
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\ \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.0263357394040558\n\
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\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
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\ \"acc\": 0.46218487394957986,\n \"acc_stderr\": 0.032385469487589795,\n\
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\ \"acc_norm\": 0.46218487394957986,\n \"acc_norm_stderr\": 0.032385469487589795\n\
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\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
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: 0.26490066225165565,\n \"acc_stderr\": 0.036030385453603826,\n \"\
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acc_norm\": 0.26490066225165565,\n \"acc_norm_stderr\": 0.036030385453603826\n\
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\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
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: 0.6275229357798165,\n \"acc_stderr\": 0.020728368457638497,\n \"\
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acc_norm\": 0.6275229357798165,\n \"acc_norm_stderr\": 0.020728368457638497\n\
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\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
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: 0.2916666666666667,\n \"acc_stderr\": 0.03099866630456052,\n \"\
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acc_norm\": 0.2916666666666667,\n \"acc_norm_stderr\": 0.03099866630456052\n\
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\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
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: 0.6078431372549019,\n \"acc_stderr\": 0.03426712349247273,\n \"\
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acc_norm\": 0.6078431372549019,\n \"acc_norm_stderr\": 0.03426712349247273\n\
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\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
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acc\": 0.70042194092827,\n \"acc_stderr\": 0.029818024749753095,\n \
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\ \"acc_norm\": 0.70042194092827,\n \"acc_norm_stderr\": 0.029818024749753095\n\
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\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6143497757847534,\n\
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\ \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.6143497757847534,\n\
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\ \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
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: {\n \"acc\": 0.5572519083969466,\n \"acc_stderr\": 0.0435644720266507,\n\
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\ \"acc_norm\": 0.5572519083969466,\n \"acc_norm_stderr\": 0.0435644720266507\n\
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\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
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\ 0.6776859504132231,\n \"acc_stderr\": 0.042664163633521685,\n \"\
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acc_norm\": 0.6776859504132231,\n \"acc_norm_stderr\": 0.042664163633521685\n\
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\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5555555555555556,\n\
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\ \"acc_stderr\": 0.04803752235190193,\n \"acc_norm\": 0.5555555555555556,\n\
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\ \"acc_norm_stderr\": 0.04803752235190193\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
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: {\n \"acc\": 0.5214723926380368,\n \"acc_stderr\": 0.03924746876751128,\n\
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\ \"acc_norm\": 0.5214723926380368,\n \"acc_norm_stderr\": 0.03924746876751128\n\
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\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n\
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\ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\
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\ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\"\
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: {\n \"acc\": 0.6796116504854369,\n \"acc_stderr\": 0.04620284082280041,\n\
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\ \"acc_norm\": 0.6796116504854369,\n \"acc_norm_stderr\": 0.04620284082280041\n\
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\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7435897435897436,\n\
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\ \"acc_stderr\": 0.028605953702004243,\n \"acc_norm\": 0.7435897435897436,\n\
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\ \"acc_norm_stderr\": 0.028605953702004243\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
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: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
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\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
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\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6922094508301405,\n\
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\ \"acc_stderr\": 0.016506045045155633,\n \"acc_norm\": 0.6922094508301405,\n\
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\ \"acc_norm_stderr\": 0.016506045045155633\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
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: {\n \"acc\": 0.5115606936416185,\n \"acc_stderr\": 0.026911898686377913,\n\
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\ \"acc_norm\": 0.5115606936416185,\n \"acc_norm_stderr\": 0.026911898686377913\n\
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\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23910614525139665,\n\
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\ \"acc_stderr\": 0.014265554192331144,\n \"acc_norm\": 0.23910614525139665,\n\
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\ \"acc_norm_stderr\": 0.014265554192331144\n },\n \"harness|hendrycksTest-nutrition|5\"\
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: {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.028607893699576073,\n\
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\ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.028607893699576073\n\
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\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5337620578778135,\n\
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\ \"acc_stderr\": 0.02833327710956279,\n \"acc_norm\": 0.5337620578778135,\n\
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\ \"acc_norm_stderr\": 0.02833327710956279\n },\n \"harness|hendrycksTest-prehistory|5\"\
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: {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.027777777777777797,\n\
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\ \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.027777777777777797\n\
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\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
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acc\": 0.34397163120567376,\n \"acc_stderr\": 0.02833801742861133,\n \
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\ \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.02833801742861133\n\
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\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.37809647979139505,\n\
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\ \"acc_stderr\": 0.012384878406798095,\n \"acc_norm\": 0.37809647979139505,\n\
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\ \"acc_norm_stderr\": 0.012384878406798095\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
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: {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.03035230339535196,\n\
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\ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.03035230339535196\n\
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\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
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acc\": 0.48856209150326796,\n \"acc_stderr\": 0.020222541515610863,\n \
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\ \"acc_norm\": 0.48856209150326796,\n \"acc_norm_stderr\": 0.020222541515610863\n\
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\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5818181818181818,\n\
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\ \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n\
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\ \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness|hendrycksTest-security_studies|5\"\
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: {\n \"acc\": 0.4816326530612245,\n \"acc_stderr\": 0.03198761546763126,\n\
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\ \"acc_norm\": 0.4816326530612245,\n \"acc_norm_stderr\": 0.03198761546763126\n\
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\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6716417910447762,\n\
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\ \"acc_stderr\": 0.03320685889744324,\n \"acc_norm\": 0.6716417910447762,\n\
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\ \"acc_norm_stderr\": 0.03320685889744324\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
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: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
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\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
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\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.40963855421686746,\n\
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\ \"acc_stderr\": 0.03828401115079021,\n \"acc_norm\": 0.40963855421686746,\n\
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\ \"acc_norm_stderr\": 0.03828401115079021\n },\n \"harness|hendrycksTest-world_religions|5\"\
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: {\n \"acc\": 0.695906432748538,\n \"acc_stderr\": 0.0352821125824523,\n\
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\ \"acc_norm\": 0.695906432748538,\n \"acc_norm_stderr\": 0.0352821125824523\n\
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\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.31334149326805383,\n\
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\ \"mc1_stderr\": 0.016238065069059608,\n \"mc2\": 0.454194299460972,\n\
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\ \"mc2_stderr\": 0.014407901135836145\n }\n}\n```"
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repo_url: https://huggingface.co/xzuyn/Alpacino-SuperCOT-13B
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leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
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point_of_contact: clementine@hf.co
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- split: latest
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path:
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- '**/details_harness|arc:challenge|25_2023-07-18T14:16:23.975101.parquet'
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- config_name: harness_hellaswag_10
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data_files:
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- split: 2023_07_18T14_16_23.975101
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@@ -782,14 +646,25 @@ configs:
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- split: latest
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path:
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- '**/details_harness|truthfulqa:mc|0_2023-07-18T14:16:23.975101.parquet'
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- config_name: results
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data_files:
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- split: 2023_07_18T14_16_23.975101
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path:
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| 789 |
- results_2023-07-18T14:16:23.975101.parquet
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- split: latest
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path:
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-
- results_2023-
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| 793 |
---
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| 794 |
|
| 795 |
# Dataset Card for Evaluation run of xzuyn/Alpacino-SuperCOT-13B
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|
@@ -806,9 +681,9 @@ configs:
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| 806 |
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| 807 |
Dataset automatically created during the evaluation run of model [xzuyn/Alpacino-SuperCOT-13B](https://huggingface.co/xzuyn/Alpacino-SuperCOT-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
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-
The dataset is composed of
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-
The dataset has been created from
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An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
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@@ -816,385 +691,37 @@ To load the details from a run, you can for instance do the following:
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| 816 |
```python
|
| 817 |
from datasets import load_dataset
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| 818 |
data = load_dataset("open-llm-leaderboard/details_xzuyn__Alpacino-SuperCOT-13B",
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-
"
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| 820 |
split="train")
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| 821 |
```
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| 822 |
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| 823 |
## Latest results
|
| 824 |
|
| 825 |
-
These are the [latest results from run 2023-
|
| 826 |
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| 827 |
```python
|
| 828 |
{
|
| 829 |
"all": {
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| 830 |
-
"
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-
"
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"
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"
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"
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"
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"
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"
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-
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-
"
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-
"
|
| 850 |
-
},
|
| 851 |
-
"harness|hendrycksTest-abstract_algebra|5": {
|
| 852 |
-
"acc": 0.33,
|
| 853 |
-
"acc_stderr": 0.04725815626252606,
|
| 854 |
-
"acc_norm": 0.33,
|
| 855 |
-
"acc_norm_stderr": 0.04725815626252606
|
| 856 |
-
},
|
| 857 |
-
"harness|hendrycksTest-anatomy|5": {
|
| 858 |
-
"acc": 0.4888888888888889,
|
| 859 |
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"acc_stderr": 0.04318275491977976,
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| 860 |
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"acc_norm": 0.4888888888888889,
|
| 861 |
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"acc_norm_stderr": 0.04318275491977976
|
| 862 |
-
},
|
| 863 |
-
"harness|hendrycksTest-astronomy|5": {
|
| 864 |
-
"acc": 0.5,
|
| 865 |
-
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|
| 866 |
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"acc_norm": 0.5,
|
| 867 |
-
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|
| 868 |
-
},
|
| 869 |
-
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|
| 870 |
-
"acc": 0.47,
|
| 871 |
-
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|
| 872 |
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"acc_norm": 0.47,
|
| 873 |
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"acc_norm_stderr": 0.050161355804659205
|
| 874 |
-
},
|
| 875 |
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"harness|hendrycksTest-clinical_knowledge|5": {
|
| 876 |
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|
| 877 |
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| 878 |
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"acc_norm": 0.4981132075471698,
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| 879 |
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|
| 880 |
-
},
|
| 881 |
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"harness|hendrycksTest-college_biology|5": {
|
| 882 |
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|
| 883 |
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|
| 884 |
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"acc_norm": 0.4513888888888889,
|
| 885 |
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|
| 886 |
-
},
|
| 887 |
-
"harness|hendrycksTest-college_chemistry|5": {
|
| 888 |
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"acc": 0.33,
|
| 889 |
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"acc_stderr": 0.04725815626252604,
|
| 890 |
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"acc_norm": 0.33,
|
| 891 |
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"acc_norm_stderr": 0.04725815626252604
|
| 892 |
-
},
|
| 893 |
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"harness|hendrycksTest-college_computer_science|5": {
|
| 894 |
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"acc": 0.44,
|
| 895 |
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"acc_stderr": 0.04988876515698589,
|
| 896 |
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"acc_norm": 0.44,
|
| 897 |
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"acc_norm_stderr": 0.04988876515698589
|
| 898 |
-
},
|
| 899 |
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"harness|hendrycksTest-college_mathematics|5": {
|
| 900 |
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"acc": 0.3,
|
| 901 |
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|
| 902 |
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"acc_norm": 0.3,
|
| 903 |
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"acc_norm_stderr": 0.046056618647183814
|
| 904 |
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|
| 905 |
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"harness|hendrycksTest-college_medicine|5": {
|
| 906 |
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|
| 907 |
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|
| 910 |
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|
| 911 |
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|
| 912 |
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| 916 |
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|
| 917 |
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"harness|hendrycksTest-computer_security|5": {
|
| 918 |
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"acc": 0.6,
|
| 919 |
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|
| 920 |
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"acc_norm": 0.6,
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| 921 |
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|
| 922 |
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|
| 923 |
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"harness|hendrycksTest-conceptual_physics|5": {
|
| 924 |
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|
| 925 |
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| 928 |
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|
| 929 |
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|
| 930 |
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|
| 934 |
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|
| 935 |
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"harness|hendrycksTest-electrical_engineering|5": {
|
| 936 |
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|
| 940 |
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|
| 941 |
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|
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| 946 |
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| 947 |
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| 948 |
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| 949 |
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| 952 |
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| 953 |
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|
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| 958 |
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|
| 960 |
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| 964 |
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| 970 |
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| 971 |
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| 976 |
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| 977 |
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"harness|hendrycksTest-high_school_european_history|5": {
|
| 978 |
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| 982 |
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| 983 |
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| 987 |
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|
| 988 |
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},
|
| 989 |
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"harness|hendrycksTest-high_school_government_and_politics|5": {
|
| 990 |
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|
| 991 |
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| 994 |
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|
| 996 |
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| 999 |
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| 1000 |
-
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|
| 1001 |
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|
| 1002 |
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| 1003 |
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| 1005 |
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| 1006 |
-
},
|
| 1007 |
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"harness|hendrycksTest-high_school_microeconomics|5": {
|
| 1008 |
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| 1009 |
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| 1012 |
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| 1013 |
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|
| 1014 |
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| 1015 |
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| 1017 |
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| 1018 |
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| 1019 |
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|
| 1020 |
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| 1021 |
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|
| 1024 |
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| 1025 |
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|
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| 1030 |
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|
| 1031 |
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|
| 1032 |
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|
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| 1036 |
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| 1037 |
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"harness|hendrycksTest-high_school_world_history|5": {
|
| 1038 |
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| 1039 |
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| 1042 |
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|
| 1044 |
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| 1045 |
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|
| 1048 |
-
},
|
| 1049 |
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|
| 1050 |
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| 1053 |
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|
| 1054 |
-
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|
| 1055 |
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|
| 1056 |
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|
| 1057 |
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| 1059 |
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|
| 1060 |
-
},
|
| 1061 |
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"harness|hendrycksTest-jurisprudence|5": {
|
| 1062 |
-
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|
| 1063 |
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| 1065 |
-
"acc_norm_stderr": 0.04803752235190193
|
| 1066 |
-
},
|
| 1067 |
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"harness|hendrycksTest-logical_fallacies|5": {
|
| 1068 |
-
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|
| 1069 |
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"acc_norm": 0.5214723926380368,
|
| 1071 |
-
"acc_norm_stderr": 0.03924746876751128
|
| 1072 |
-
},
|
| 1073 |
-
"harness|hendrycksTest-machine_learning|5": {
|
| 1074 |
-
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|
| 1075 |
-
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|
| 1076 |
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|
| 1077 |
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|
| 1078 |
-
},
|
| 1079 |
-
"harness|hendrycksTest-management|5": {
|
| 1080 |
-
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|
| 1081 |
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|
| 1082 |
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"acc_norm": 0.6796116504854369,
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| 1083 |
-
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|
| 1084 |
-
},
|
| 1085 |
-
"harness|hendrycksTest-marketing|5": {
|
| 1086 |
-
"acc": 0.7435897435897436,
|
| 1087 |
-
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|
| 1089 |
-
"acc_norm_stderr": 0.028605953702004243
|
| 1090 |
-
},
|
| 1091 |
-
"harness|hendrycksTest-medical_genetics|5": {
|
| 1092 |
-
"acc": 0.5,
|
| 1093 |
-
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|
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| 1095 |
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|
| 1096 |
-
},
|
| 1097 |
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"harness|hendrycksTest-miscellaneous|5": {
|
| 1098 |
-
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|
| 1099 |
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| 1100 |
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"acc_norm": 0.6922094508301405,
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| 1101 |
-
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|
| 1102 |
-
},
|
| 1103 |
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"harness|hendrycksTest-moral_disputes|5": {
|
| 1104 |
-
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|
| 1105 |
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| 1107 |
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|
| 1108 |
-
},
|
| 1109 |
-
"harness|hendrycksTest-moral_scenarios|5": {
|
| 1110 |
-
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|
| 1111 |
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|
| 1112 |
-
"acc_norm": 0.23910614525139665,
|
| 1113 |
-
"acc_norm_stderr": 0.014265554192331144
|
| 1114 |
-
},
|
| 1115 |
-
"harness|hendrycksTest-nutrition|5": {
|
| 1116 |
-
"acc": 0.5196078431372549,
|
| 1117 |
-
"acc_stderr": 0.028607893699576073,
|
| 1118 |
-
"acc_norm": 0.5196078431372549,
|
| 1119 |
-
"acc_norm_stderr": 0.028607893699576073
|
| 1120 |
-
},
|
| 1121 |
-
"harness|hendrycksTest-philosophy|5": {
|
| 1122 |
-
"acc": 0.5337620578778135,
|
| 1123 |
-
"acc_stderr": 0.02833327710956279,
|
| 1124 |
-
"acc_norm": 0.5337620578778135,
|
| 1125 |
-
"acc_norm_stderr": 0.02833327710956279
|
| 1126 |
-
},
|
| 1127 |
-
"harness|hendrycksTest-prehistory|5": {
|
| 1128 |
-
"acc": 0.5277777777777778,
|
| 1129 |
-
"acc_stderr": 0.027777777777777797,
|
| 1130 |
-
"acc_norm": 0.5277777777777778,
|
| 1131 |
-
"acc_norm_stderr": 0.027777777777777797
|
| 1132 |
-
},
|
| 1133 |
-
"harness|hendrycksTest-professional_accounting|5": {
|
| 1134 |
-
"acc": 0.34397163120567376,
|
| 1135 |
-
"acc_stderr": 0.02833801742861133,
|
| 1136 |
-
"acc_norm": 0.34397163120567376,
|
| 1137 |
-
"acc_norm_stderr": 0.02833801742861133
|
| 1138 |
-
},
|
| 1139 |
-
"harness|hendrycksTest-professional_law|5": {
|
| 1140 |
-
"acc": 0.37809647979139505,
|
| 1141 |
-
"acc_stderr": 0.012384878406798095,
|
| 1142 |
-
"acc_norm": 0.37809647979139505,
|
| 1143 |
-
"acc_norm_stderr": 0.012384878406798095
|
| 1144 |
-
},
|
| 1145 |
-
"harness|hendrycksTest-professional_medicine|5": {
|
| 1146 |
-
"acc": 0.5183823529411765,
|
| 1147 |
-
"acc_stderr": 0.03035230339535196,
|
| 1148 |
-
"acc_norm": 0.5183823529411765,
|
| 1149 |
-
"acc_norm_stderr": 0.03035230339535196
|
| 1150 |
-
},
|
| 1151 |
-
"harness|hendrycksTest-professional_psychology|5": {
|
| 1152 |
-
"acc": 0.48856209150326796,
|
| 1153 |
-
"acc_stderr": 0.020222541515610863,
|
| 1154 |
-
"acc_norm": 0.48856209150326796,
|
| 1155 |
-
"acc_norm_stderr": 0.020222541515610863
|
| 1156 |
-
},
|
| 1157 |
-
"harness|hendrycksTest-public_relations|5": {
|
| 1158 |
-
"acc": 0.5818181818181818,
|
| 1159 |
-
"acc_stderr": 0.04724577405731572,
|
| 1160 |
-
"acc_norm": 0.5818181818181818,
|
| 1161 |
-
"acc_norm_stderr": 0.04724577405731572
|
| 1162 |
-
},
|
| 1163 |
-
"harness|hendrycksTest-security_studies|5": {
|
| 1164 |
-
"acc": 0.4816326530612245,
|
| 1165 |
-
"acc_stderr": 0.03198761546763126,
|
| 1166 |
-
"acc_norm": 0.4816326530612245,
|
| 1167 |
-
"acc_norm_stderr": 0.03198761546763126
|
| 1168 |
-
},
|
| 1169 |
-
"harness|hendrycksTest-sociology|5": {
|
| 1170 |
-
"acc": 0.6716417910447762,
|
| 1171 |
-
"acc_stderr": 0.03320685889744324,
|
| 1172 |
-
"acc_norm": 0.6716417910447762,
|
| 1173 |
-
"acc_norm_stderr": 0.03320685889744324
|
| 1174 |
-
},
|
| 1175 |
-
"harness|hendrycksTest-us_foreign_policy|5": {
|
| 1176 |
-
"acc": 0.75,
|
| 1177 |
-
"acc_stderr": 0.04351941398892446,
|
| 1178 |
-
"acc_norm": 0.75,
|
| 1179 |
-
"acc_norm_stderr": 0.04351941398892446
|
| 1180 |
-
},
|
| 1181 |
-
"harness|hendrycksTest-virology|5": {
|
| 1182 |
-
"acc": 0.40963855421686746,
|
| 1183 |
-
"acc_stderr": 0.03828401115079021,
|
| 1184 |
-
"acc_norm": 0.40963855421686746,
|
| 1185 |
-
"acc_norm_stderr": 0.03828401115079021
|
| 1186 |
-
},
|
| 1187 |
-
"harness|hendrycksTest-world_religions|5": {
|
| 1188 |
-
"acc": 0.695906432748538,
|
| 1189 |
-
"acc_stderr": 0.0352821125824523,
|
| 1190 |
-
"acc_norm": 0.695906432748538,
|
| 1191 |
-
"acc_norm_stderr": 0.0352821125824523
|
| 1192 |
-
},
|
| 1193 |
-
"harness|truthfulqa:mc|0": {
|
| 1194 |
-
"mc1": 0.31334149326805383,
|
| 1195 |
-
"mc1_stderr": 0.016238065069059608,
|
| 1196 |
-
"mc2": 0.454194299460972,
|
| 1197 |
-
"mc2_stderr": 0.014407901135836145
|
| 1198 |
}
|
| 1199 |
}
|
| 1200 |
```
|
|
|
|
| 3 |
dataset_summary: "Dataset automatically created during the evaluation run of model\
|
| 4 |
\ [xzuyn/Alpacino-SuperCOT-13B](https://huggingface.co/xzuyn/Alpacino-SuperCOT-13B)\
|
| 5 |
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
|
| 6 |
+
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
|
| 7 |
+
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
|
| 8 |
\ found as a specific split in each configuration, the split being named using the\
|
| 9 |
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
|
| 10 |
\nAn additional configuration \"results\" store all the aggregated results of the\
|
|
|
|
| 12 |
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
|
| 13 |
\nTo load the details from a run, you can for instance do the following:\n```python\n\
|
| 14 |
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_xzuyn__Alpacino-SuperCOT-13B\"\
|
| 15 |
+
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
|
| 16 |
+
These are the [latest results from run 2023-10-14T21:25:30.263923](https://huggingface.co/datasets/open-llm-leaderboard/details_xzuyn__Alpacino-SuperCOT-13B/blob/main/results_2023-10-14T21-25-30.263923.json)(note\
|
| 17 |
+
\ that their might be results for other tasks in the repos if successive evals didn't\
|
| 18 |
+
\ cover the same tasks. You find each in the results and the \"latest\" split for\
|
| 19 |
+
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.034710570469798654,\n\
|
| 20 |
+
\ \"em_stderr\": 0.001874559812477868,\n \"f1\": 0.09780201342281894,\n\
|
| 21 |
+
\ \"f1_stderr\": 0.002213982622562692,\n \"acc\": 0.4222955971643869,\n\
|
| 22 |
+
\ \"acc_stderr\": 0.00954675265516166\n },\n \"harness|drop|3\": {\n\
|
| 23 |
+
\ \"em\": 0.034710570469798654,\n \"em_stderr\": 0.001874559812477868,\n\
|
| 24 |
+
\ \"f1\": 0.09780201342281894,\n \"f1_stderr\": 0.002213982622562692\n\
|
| 25 |
+
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07505686125852919,\n \
|
| 26 |
+
\ \"acc_stderr\": 0.007257633145486642\n },\n \"harness|winogrande|5\"\
|
| 27 |
+
: {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836676\n\
|
| 28 |
+
\ }\n}\n```"
|
|
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|
|
| 29 |
repo_url: https://huggingface.co/xzuyn/Alpacino-SuperCOT-13B
|
| 30 |
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
|
| 31 |
point_of_contact: clementine@hf.co
|
|
|
|
| 38 |
- split: latest
|
| 39 |
path:
|
| 40 |
- '**/details_harness|arc:challenge|25_2023-07-18T14:16:23.975101.parquet'
|
| 41 |
+
- config_name: harness_drop_3
|
| 42 |
+
data_files:
|
| 43 |
+
- split: 2023_10_14T21_25_30.263923
|
| 44 |
+
path:
|
| 45 |
+
- '**/details_harness|drop|3_2023-10-14T21-25-30.263923.parquet'
|
| 46 |
+
- split: latest
|
| 47 |
+
path:
|
| 48 |
+
- '**/details_harness|drop|3_2023-10-14T21-25-30.263923.parquet'
|
| 49 |
+
- config_name: harness_gsm8k_5
|
| 50 |
+
data_files:
|
| 51 |
+
- split: 2023_10_14T21_25_30.263923
|
| 52 |
+
path:
|
| 53 |
+
- '**/details_harness|gsm8k|5_2023-10-14T21-25-30.263923.parquet'
|
| 54 |
+
- split: latest
|
| 55 |
+
path:
|
| 56 |
+
- '**/details_harness|gsm8k|5_2023-10-14T21-25-30.263923.parquet'
|
| 57 |
- config_name: harness_hellaswag_10
|
| 58 |
data_files:
|
| 59 |
- split: 2023_07_18T14_16_23.975101
|
|
|
|
| 646 |
- split: latest
|
| 647 |
path:
|
| 648 |
- '**/details_harness|truthfulqa:mc|0_2023-07-18T14:16:23.975101.parquet'
|
| 649 |
+
- config_name: harness_winogrande_5
|
| 650 |
+
data_files:
|
| 651 |
+
- split: 2023_10_14T21_25_30.263923
|
| 652 |
+
path:
|
| 653 |
+
- '**/details_harness|winogrande|5_2023-10-14T21-25-30.263923.parquet'
|
| 654 |
+
- split: latest
|
| 655 |
+
path:
|
| 656 |
+
- '**/details_harness|winogrande|5_2023-10-14T21-25-30.263923.parquet'
|
| 657 |
- config_name: results
|
| 658 |
data_files:
|
| 659 |
- split: 2023_07_18T14_16_23.975101
|
| 660 |
path:
|
| 661 |
- results_2023-07-18T14:16:23.975101.parquet
|
| 662 |
+
- split: 2023_10_14T21_25_30.263923
|
| 663 |
+
path:
|
| 664 |
+
- results_2023-10-14T21-25-30.263923.parquet
|
| 665 |
- split: latest
|
| 666 |
path:
|
| 667 |
+
- results_2023-10-14T21-25-30.263923.parquet
|
| 668 |
---
|
| 669 |
|
| 670 |
# Dataset Card for Evaluation run of xzuyn/Alpacino-SuperCOT-13B
|
|
|
|
| 681 |
|
| 682 |
Dataset automatically created during the evaluation run of model [xzuyn/Alpacino-SuperCOT-13B](https://huggingface.co/xzuyn/Alpacino-SuperCOT-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
|
| 683 |
|
| 684 |
+
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
|
| 685 |
|
| 686 |
+
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
|
| 687 |
|
| 688 |
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
|
| 689 |
|
|
|
|
| 691 |
```python
|
| 692 |
from datasets import load_dataset
|
| 693 |
data = load_dataset("open-llm-leaderboard/details_xzuyn__Alpacino-SuperCOT-13B",
|
| 694 |
+
"harness_winogrande_5",
|
| 695 |
split="train")
|
| 696 |
```
|
| 697 |
|
| 698 |
## Latest results
|
| 699 |
|
| 700 |
+
These are the [latest results from run 2023-10-14T21:25:30.263923](https://huggingface.co/datasets/open-llm-leaderboard/details_xzuyn__Alpacino-SuperCOT-13B/blob/main/results_2023-10-14T21-25-30.263923.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
|
| 701 |
|
| 702 |
```python
|
| 703 |
{
|
| 704 |
"all": {
|
| 705 |
+
"em": 0.034710570469798654,
|
| 706 |
+
"em_stderr": 0.001874559812477868,
|
| 707 |
+
"f1": 0.09780201342281894,
|
| 708 |
+
"f1_stderr": 0.002213982622562692,
|
| 709 |
+
"acc": 0.4222955971643869,
|
| 710 |
+
"acc_stderr": 0.00954675265516166
|
| 711 |
+
},
|
| 712 |
+
"harness|drop|3": {
|
| 713 |
+
"em": 0.034710570469798654,
|
| 714 |
+
"em_stderr": 0.001874559812477868,
|
| 715 |
+
"f1": 0.09780201342281894,
|
| 716 |
+
"f1_stderr": 0.002213982622562692
|
| 717 |
+
},
|
| 718 |
+
"harness|gsm8k|5": {
|
| 719 |
+
"acc": 0.07505686125852919,
|
| 720 |
+
"acc_stderr": 0.007257633145486642
|
| 721 |
+
},
|
| 722 |
+
"harness|winogrande|5": {
|
| 723 |
+
"acc": 0.7695343330702447,
|
| 724 |
+
"acc_stderr": 0.011835872164836676
|
|
|
|
|
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| 725 |
}
|
| 726 |
}
|
| 727 |
```
|